Rent RTX 4090
Compare live on-demand and spot rental prices across 97+ cloud providers. Consumer GPU with surprisingly strong FP32. No NVLink. Best $/TFLOP for budget workloads.
Choosing the right billing model for RTX 4090
Provision and terminate at any time. Ideal for development, short experiments, and workloads with unpredictable duration.
Instances can be reclaimed when demand spikes. Best for fault-tolerant batch jobs, training with checkpointing, and preprocessing.
Lock in a rate for 1–3 months. Right for sustained production inference or long training runs where cost predictability matters.
Best use cases
- Fine-tuning small models
- Inference up to 13B
- Cost-sensitive workloads
RTX 4090 — Specs & Benchmarks
Performance bars, compute tiers (FP32/FP16/BF16/FP8/INT8), memory specs, LLM model size guidance, and related GPU comparisons.
RTX 4090 Rental Guide
The RTX 4090 is the most cost-effective GPU for AI workloads that fit within 24GB of VRAM. At $0.35–$0.75/hr on-demand — 3–5× cheaper than an A100 80GB — it delivers strong throughput for fine-tuning 7B–13B models, running quantized inference on models up to 70B (INT4/GGUF), and Stable Diffusion image generation. For development, experimentation, and cost-sensitive production inference of smaller models, it is often the best choice.
Spot instances for RTX 4090 are widely available at 30–55% below on-demand rates, particularly on Vast.ai and RunPod where consumer GPU spot markets are active. For batch inference and fine-tuning jobs with checkpointing, spot RTX 4090 instances often deliver the best cost-per-output of any GPU. On-demand is appropriate for interactive inference where uptime matters.
The key limitation to plan around is the 24GB VRAM ceiling and the lack of ECC memory. For models above 13B parameters at FP16, you will need INT8 or INT4 quantization. For production data center deployments requiring ECC, consider the A10G or L40S instead. The RTX 4090 is a consumer GPU — cloud providers running it in data centers accept the reliability trade-off in exchange for lower cost. Use the GPU cost calculator to compare total cost across RTX 4090, A10G, and L40S for your specific workload.
Frequently Asked Questions
How much does it cost to rent a RTX 4090?
RTX 4090 on-demand rental prices vary by provider and region. On-demand rates typically range based on availability and provider margins — use the comparison table above to see current live rates across all providers. Spot instances are generally 40–70% cheaper than on-demand but can be interrupted. Monthly cost estimates (hourly rate × 730 hours) are shown in the table for sustained workloads.
Which cloud provider has the cheapest RTX 4090?
The cheapest RTX 4090 provider changes as providers update their pricing. The comparison table above shows live rates sorted by price, so the cheapest option is always at the top. Factors beyond headline price include region (latency to your users), availability (high/medium/low), and billing granularity (per-second vs per-hour minimums).
What can I run on a RTX 4090?
With 24GB of GDDR6X, the RTX 4090 can run LLM models up to approximately 12B parameters at FP16, 24B at INT8, or 48B at INT4/GGUF quantization. Common workloads include: Fine-tuning small models, Inference up to 13B, Cost-sensitive workloads. Consumer GPU with surprisingly strong FP32. No NVLink. Best $/TFLOP for budget workloads.
Should I use on-demand or spot pricing for RTX 4090?
Spot instances save 40–70% vs on-demand but can be interrupted when the provider needs capacity back. Use spot for: batch inference jobs, training runs with checkpointing, preprocessing pipelines, and any workload that can tolerate interruption and restart. Use on-demand for: production inference serving, interactive workloads, and jobs that cannot be interrupted. Most providers bill per second, so short on-demand jobs are not penalized by hourly minimums.
How does the RTX 4090 compare to the H100 for cloud rental?
The H100 80GB delivers 1,979 TFLOPS FP16 with 3,350 GB/s HBM3 bandwidth, compared to the RTX 4090's 165.2 TFLOPS FP16 and 1008 GB/s bandwidth. The H100 is significantly more expensive — typically $2.50–$5.00/hr vs lower rates for the RTX 4090. For workloads that fit within 24GB and don't require FP8 precision, the RTX 4090 often delivers better cost-per-token than the H100.
What is the memory bandwidth of the RTX 4090 and why does it matter?
The RTX 4090 has 1008 GB/s of memory bandwidth. For LLM inference, memory bandwidth is often more important than raw TFLOPS — each autoregressive token generation reads the full model weight matrix from VRAM, so bandwidth directly determines tokens-per-second throughput. Higher bandwidth means faster inference for the same model at the same batch size. For batch inference (processing many requests simultaneously), compute throughput becomes more important.
Can I use the RTX 4090 for Stable Diffusion or image generation?
Yes — the RTX 4090 is well-suited for Stable Diffusion and image generation workloads. Image generation is primarily FP32 and FP16 compute-bound, and the RTX 4090's 82.6 TFLOPS FP32 throughput determines images-per-second. The 24GB VRAM fits SDXL (requires ~6GB) and most ControlNet pipelines. For high-throughput image generation at scale, compare cost-per-image across providers using the GPU cost calculator.